Decoding and Selective Modulation of Human Memory During Awake/Sleep Cycles

清醒/睡眠周期期间人类记忆的解码和选择性调制

• 批准号: 10289993
• 负责人: ITZHAK FRIED
• 金额: $ 147.85万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10289993
• 关键词: Acoustic Stimulation; Alzheimer' s Disease; Animals; Area; Artificial Intelligence; Auditory; Behavior; Brain; Brain region; Cells; Clinical; Code; Cognitive; Coupling; Data; Development; Electric Stimulation; Electrodes; Electrophysiology (science); Episodic memory; Event; Face; Foundations; Future; General Population; Goals; Hippocampus (Brain); Human; Intervention; Knowledge; Life; Light; Measures; Medial; Memory; Memory Disorders; Memory impairment; Methodology; Modeling; Nature; Neurodegenerative Disorders; Neurons; Neurosciences; Participant; Patient Monitoring; Pattern; Performance; Personal Satisfaction; Physiologic pulse; Physiological; Physiology; Play; Prevalence; Probability; Process; Protocols documentation; Research; Resolution; Response to stimulus physiology; Retrieval; Rodent; Sample Size; Sampling; Scheme; Series; Site; Sleep; Sleep Architecture; Slow-Wave Sleep; Stimulus; System; Techniques; Technology; Temporal Lobe; Testing; Therapeutic; Therapeutic Intervention; Time; Tsunami; Visual; Wakefulness; aging population; awake; deep learning; deep learning algorithm; experience; improved; innovation; interdisciplinary approach; interdisciplinary collaboration; learning strategy; memory consolidation; memory process; memory recognition; memory retrieval; movie; multidimensional data; multidisciplinary; multimodality; nervous system disorder; neural patterning; neuromechanism; novel; novel therapeutics; predictive modeling; recruit; relating to nervous system; sleep behavior; tool; white matter

PROJECT SUMMARY/ABSTRACT Episodic memories integrate the content of human experience in space and time and constitute the core of one's identity. Memory formation involves processing, and constructing interpretations of the incoming information in our daily lives and is one of the first functions compromised in neurodegenerative diseases such as Alzheimer's Disease. With population aging, we face a “Cognitive Tsunami” of millions of people with memory disorders. Thus, understanding neural mechanisms of memory, and finding interventions that enhance these processes is a critical endeavor with the potential to improve the lives of countless people world-wide. Although it is established that memory is critical for cognitive well-being, and sleep is critical for memory consolidation, the underlying mechanisms in the human brain are poorly understood. Research on memory and sleep so far has suffered from a gap between non-invasive cognitive research in humans and detailed electrophysiological research in animals. Moreover, most human studies are dominated by stimulus response methodologies where the presented stimuli constitute limited, discretized, aspects of memory. This approach, albeit well-controlled, is far from the rich narrative of episodes we experience. Thus, to mechanistically probe human memory consolidation, it is imperative to (a) employ methodologies that incorporate the continuous and multimodal nature of experience; (b) identify relevant neural activation patterns and how they are transformed and reactivated during sleep; (c) establish means to causally modulate memory processes during sleep. Building upon our exploratory U01 project, this proposal seeks a breakthrough in our understanding by going beyond the state-of-the-art, and via the application of integrative and multidisciplinary approaches. It capitalizes on a highly unique opportunity to record and modulate neuronal activity of a large number of single neurons and neuronal assemblies in the human brain. In parallel, it exploits the high dimensionality of the data as an asset through the use of cutting-edge Deep Learning (DL) algorithms, which have emerged as promising analysis tools. Specifically, the project will investigate the presence, and decoding, of distributed neural patterns associated with memory for different aspects of experience during wakefulness and identify their reactivation during sleep. The proposal aims to selectively modulate memory via application of novel closed-loop stimulation in sleep in concert with the DL model predictions. We anticipate that this project is poised to shed light on the relationship between sleep and memory, and its modulation from a novel perspective. Such an ambitious goal can only be achieved with unrivaled combination of experience, access to a clinical setting, and interdisciplinary collaborations such as those proposed in this project. By combining the opportunity to stimulate and record neural activity with the computational power of artificial intelligence, this project aims to offer findings with far reaching implications for basic neuroscience and contribute to the development of novel therapies for human memory disorders.

项目概要/摘要 情景记忆整合了人类在空间和时间上的经验内容，构成了人的记忆的核心。 身份。记忆的形成涉及对传入信息的处理和构建解释 我们的日常生活，是阿尔茨海默病等神经退行性疾病中最先受到损害的功能之一 疾病。随着人口老龄化，我们面临着数百万人患有记忆障碍的“认知海啸”。 因此，了解记忆的神经机制并找到增强这些过程的干预措施是 这是一项有潜力改善全世界无数人生活的重要努力。 尽管已经确定记忆对于认知健康至关重要，而睡眠对于记忆也至关重要 尽管我们对人类大脑的潜在机制知之甚少。记忆与研究 迄今为止，睡眠一直受到人类非侵入性认知研究和详细睡眠之间的差距的影响。 动物电生理学研究。此外，大多数人类研究以刺激反应为主 所呈现的刺激构成记忆的有限的、离散的方面的方法。这种做法， 虽然控制得很好，但与我们经历的情节的丰富叙述相去甚远。因此，要机械地探究 人类的记忆巩固，必须（a）采用结合连续和 体验的多模式性质； (b) 识别相关的神经激活模式及其转化方式 并在睡眠期间重新激活； (c) 建立在睡眠期间因果调节记忆过程的方法。 该提案以我们探索性的 U01 项目为基础，通过以下方式寻求突破我们的理解： 超越最先进的水平，并通过综合和多学科方法的应用。它 利用一个非常独特的机会来记录和调节大量的神经元活动 人脑中的单个神经元和神经元集合。同时，它利用了高维 通过使用已经出现的尖端深度学习 (DL) 算法，将数据作为资产 作为有前景的分析工具。具体来说，该项目将调查分布式的存在和解码 与清醒期间不同方面的经历的记忆相关的神经模式并识别 他们在睡眠期间重新激活。该提案旨在通过应用新颖的方法选择性地调节记忆 睡眠中的闭环刺激与 DL 模型预测一致。 我们预计这个项目将揭示睡眠和记忆之间的关系，并且 从新颖的角度对其进行调制。如此雄心勃勃的目标只有通过无与伦比的组合才能实现 经验、进入临床环境以及跨学科合作，例如本文中提出的那些 项目。通过将刺激和记录神经活动的机会与计算能力相结合 人工智能，该项目旨在提供对基础神经科学和人工智能具有深远影响的发现 有助于开发人类记忆障碍的新疗法。